Low Energy Toll Shortcut to faster Quantum State Preparation

Quantum technologies come in a wide variety of forms, from computers, sensors, and cryptographic systems to simulations and imaging systems. But one thing that all current and future quantum systems have in common is the need to achieve reliable control over physical systems such as atoms or photons. A frequently used method to prepare quantum systems in the desired quantum state is a quantum adiabatic process, but these processes often take so long that environmental noise causes the quantum state to decohere and lose its "quantumness."

To speed up quantum state preparation and minimize decoherence, physicists have devised so-called "shortcuts to adiabaticity" (STA), which refer to any process that prepares quantum states in a shorter time than adiabatic processes without losing the benefits of being adiabatic. Originally developed for simple systems consisting of a single particle, STA has recently been extended to many-body systems, which are more relevant for applications. However, the implementation of STA in many-body systems is still very challenging due to the inherent complexity of these systems.

Researchers have devised a new hybrid method for preparing quantum states for many-body systems that combines STA with optimal control. The main advantage of the new method is that it can achieve nearly perfect STA performance yet allows for significant simplification by not requiring complete knowledge of the underlying mechanisms. The method shows that it's possible to speed up quantum state preparation at a low enough cost to justify the quantum shortcut.

Arxiv - Shortcut to Adiabaticity in the Lipkin-Meshkov-Glick Model

We study transitionless quantum driving in an infinite-range many-body system described by the Lipkin-Meshkov-Glick model. Despite the correlation length being always infinite the closing of the gap at the critical point makes the driving Hamiltonian of increasing complexity also in this case. To this aim we develop a hybrid strategy combining shortcut to adiabaticity and optimal control that allows us to achieve remarkably good performance in suppressing the defect production across the phase transition.

New Dwave Systems Quantum Computer Videos

Seeing the D-Wave facilities first-hand is a very cool experience. They look a lot like computers did back in the 60s

D-Wave is making fantastic progress in fabricating ever-larger processors. In fact, they will be releasing our new 1,152-qubit “Washington” processor in March later of this year. So they are all very excited about that. However, size (i.e., qubit-count) is not the only aspect of the processor that has been improved. We have also lowered the noise and stretched the energy scale of the qubits (making them inherently more quantum mechanical), and we have strengthened our ability to create chains of qubits (making it easier to program the processor by locking qubits together to change the effective topology of the chip). Our initial performance tests have gone really well, and we are seeing some very exciting performance from the new processor. We are now perfecting new benchmark problems and new performance metrics that more clearly showcase the innate capabilities of the Washington system. These studies, and more, will be rolling out later in the year.

The Washington chip has 2048 physical qubits but they will release with 1152 active qubits.

D-Wave’s main experimental facility is shown in a new video.

They show the cooling system, the electromagnetic shielding and how the electronics system programs the quantum processor.

Washington quantum computer chip

Quantum computer resistant version of public key encryption from modified knapsack code

- Washington State University mathematicians have designed an encryption code capable of fending off the phenomenal hacking power of a quantum computer.

Using high-level number theory and cryptography, the researchers reworked an infamous old cipher called the knapsack code to create an online security system better prepared for future demands.

Quantum computers operate on the subatomic level and theoretically provide processing power that could be exponentially faster than classical computers. Several companies are in the race to develop quantum computers including Google and Dwave Systems.

The currently popular internet security algorithms are nor resistant to quantum computers. Online transactions ranging from buying a book on Amazon to simply sending an email would need to be upgraded with new algorithms if quantum computers are successful.

A new public key code

Looking to protect future online information, Hamlin and retired mathematics professor William Webb turned to the long-abandoned knapsack code. To bring it up to quantum level - and possibly use it as a new type of public key encryption - the researchers first engineered new numbering systems for the code.

"We used alternate ways of representing numbers," said Hamlin.

In effect, they created new digital systems with much greater complexity than society's day-to-day decimal and binary systems.

"By using very complicated number strings, we produced a new version of the knapsack code that can't be broken by the usual cyber attack methods," said Webb.

As a result, Hamlin and Webb believe the redesigned knapsack code could offer a viable alternative for public key encryption with quantum computing.

Arxiv - A Knapsack-like Code Using Recurrence Sequence Representations

Abstract

We had recently shown that every positive integer can be represented uniquely using a recurrence sequence, when certain restrictions on the digit strings are satisfied. We present the details of how such representations can be used to build a knapsack-like public key cryptosystem. We also present new disguising methods, and provide arguments for the security of the code against known methods of attack.

Dwave Systems will be commercially releasing a new 1152 qubit quantum annealing system in March 2015

Dr. Colin P. Williams [CPW], Director of Business Development and Strategic Partnerships for D-Wave Systems provided answers in an email interview with Nextbigfuture.

1. How is Dwave doing with the 2048 qubit system ?

[CPW] D-Wave is making fantastic progress in fabricating ever-larger processors. In fact, we will be releasing our new 1,152-qubit “Washington” processor in March of this year. So we’re all very excited about that. However, size (i.e., qubit-count) is not the only aspect of the processor that has been improved. We have also lowered the noise and stretched the energy scale of the qubits (making them inherently more quantum mechanical), and we have strengthened our ability to create chains of qubits (making it easier to program the processor by locking qubits together to change the effective topology of the chip). Our initial performance tests have gone really well, and we are seeing some very exciting performance from the new processor. We are now perfecting new benchmark problems and new performance metrics that more clearly showcase the innate capabilities of the Washington system. These studies, and more, will be rolling out later in the year. So stay tuned for that.

Previously Nextbigfuture reported that Dwave has shown a chip with 2048 physical qubits. This is the same chip but only 1152 qubits will be active.

Dwave Systems shows off quantum chip with 2048 physical qubits

Dwave Systems has released pictures of a new quantum computer system chip with 2048 physical qubits. These are C16 chips — 16*16*8 = 2,048 physical qubits.

Researchers who work with DWave are just starting to provide some advance results on DWave's 1152 qubit system.

From Dwave CTO Geordie Rose, a subset of the qubits physically available on the chip are currently under test, corresponding to a 12×12 unit cell subgraph. However all the qubits can be activated, it’s just easier to use a smaller region for calibration and test. The D-Wave Three product will contain Washington-style chips. The first ones will ship in early 2015.

DWave redesigns and rebuilds each of the chips several times over the 12-24 months where they test and refine chips of a particular qubit size scale.

The next generation of chips are the 1152 qubit versions and are called the Washington generation. These are very early days for the Washington generation. Things will get a lot better on this one before it’s released (Rainier and Vesuvius both took 7 generations of iteration before they stabilized).

Power of Quantum Algorithms and Open Questions

Quantum algorithms - what we can do and what could we do

Mathematical Challenges in Quantum Algorithms (51 pages)

There are many things we can do with our quantum computers. For example:
Factorise large integers and hence break RSA;
Efficiently simulate quantum-mechanical systems;
Solve certain search and optimisation problems faster than possible classically;
. . .

See the Quantum Algorithm Zoo
(http://math.nist.gov/quantum/zoo/) for 214 219
papers on quantum algorithms

Advance Results on the 1152 Qubit DWave Quantum Systems up to 933 Qubits and application to cryptology

Colin Williams recently presented some new results in the UK of next generation of DWave chips. The next generation of chips are the 1152 qubit versions and are called the Washington generation. Here you can see some advance looks at the first results on up to 933 qubits. These are very early days for the Washington generation. Things will get a lot better on this one before it’s released (Rainier and Vesuvius both took 7 generations of iteration before they stabilized). But some good results on the first few prototypes.

State-of-the-Art Quantum Annealing and its Application to Cryptology

Dwave says papers coming showing speedup over classical in narrow cases by their quantum computers

Some important third-party results [will soon be published] that can demonstrate speedups over classical algorithms in certain narrow cases by DWave Quantum computers. Those are results that you won’t see from DWave; it’s more credible for one of our partners to do that. It is going to be an important milestone for Dwave. If you want to do something practical with quantum computing, DWAve is the only place you can come.

They have 12 machines running now. A few of them are online; they have customers who can access a machine over the Internet.

They have seen results that are better than classical systems. Customers have their application and integrate quantum computing into it and it performs better.

DWave Systems implies that conclusive quantum speedup will be shown with their next 1152 qubit quantum chip

DWave Systems CEO Vern Brownell says we are at the dawn of the quantum computing age, so things will change over time, and we’ll see a broader and broader set of applications. But today DWave focuses on three problem domains that they think are best suited to this particular type of quantum computing.

1. Machine learning, which is one of the most interesting things going on in computer science today. AI 2.0 and useful AI have really revolutionized the way a lot of folks do things today.

2. A broad set of optimization problems. In logistics, for example, you’re trying to find optimal routing and things like that. They are very complex and scale very quickly with the number of variables and interrelationships you’re trying to optimize for.

3. Sampling. The best example for this is perhaps in financial services, where Monte Carlo simulations represent the largest workloads in most investment banks. It’s used to model things like risk in portfolios — and that’s a fit that works very well with this type of computer.

They are particularly excited about things like working with DNA-SEQ to find better cancer cures, or doing financial modeling, or, with Lockheed in particular, helping them verify their flight control systems.

Brownell says the 1152 qubit chip end all doubt that Dwave has leaped ahead of classical systems — and will forever leave them behind.

If the DWave machines are achieving “quantum speedup” and growth curves keep pace with predictions, Jurvetson believes we could be on the precipice of a fundamental shift in computing — an exponential upon exponential leap that reshapes our assumptions about what machines can do.

The 1152 qubit chip will be released early in 2015 and four of the 1152 qubit systems have been in testing and development for over 12 months. If clear speedup is being achieved then DWave would be seeing it in their tests. The scale up from 128 qubits to 512 qubits seem be produce speedups on the order of ten thousand times. A similar leap and faster loading and readout of problems should show clear advantages in their testing.

Google plans quantum computer with longer coherence times

IEEE Spectrum has more information about Google's quantum computer hardware plans.

The Martinis group had previously built quantum computing systems of up to nine qubits based on superconducting quantum circuits—the same type of general hardware design used by D-Wave's machines. Under the new Google effort, Martinis hopes his team can roughly double the number of qubits every year and eventually work up to 40 or 80 qubits through "brute-force" scaling. "Forty qubits is a large enough number so that you can really tell if the device is going to give any interesting performance," Martinis says.

Martinis and his team will continue developing error-correction codes for Google with the aim of uncovering and fixing errors in universal logic-gate quantum computers. In May, they demonstrated a type of error-correction code called surface code that can work with lower accuracy thresholds for quantum logic operations.

So about two years to 40 qubits and three years 80 qubits.

Dwave will be commercially releasing their 1152 qubit system this year. The current model processes 512 qubits, but the new hardware will manage 1,152. That may seem like a strange number, but the hardware units can each handle eight qubits and the system stacks them in a 12 by 12 grid. [8 *144 = 1152]

They should have a 2300 qubit system next year. Dwave Systems is still improving their qubits and hardware systems.

Optimizing performance and working around limitation of Dwave Quantum Annealing Computers

Discrete optimization using quantum annealing on sparse Ising models

This paper discusses techniques for solving discrete optimization problems using quantum annealing. Practical issues likely to affect the computation include precision limitations, finite temperature, bounded energy range, sparse connectivity, and small numbers of qubits. To address these concerns they propose a way of finding energy representations with large classical gaps between ground and first excited states, efficient algorithms for mapping non-compatible Ising models into the hardware, and the use of decomposition methods for problems that are too large to fit in hardware. They validate the approach by describing experiments with D-Wave quantum hardware for low density parity check decoding with up to 1000 variables.

Reexamining classical and quantum models for the D-Wave One processor

Arxiv - Reexamining classical and quantum models for the D-Wave One processor (18 pages)

USC Researchers revisit the evidence for quantum annealing in the D-Wave One device (DW1) based on the study of random Ising instances. Using the probability distributions of finding the ground states of such instances, previous work found agreement with both simulated quantum annealing (SQA) and a classical rotor model. Thus the DW1 ground state success probabilities are consistent with both models, and a different measure is needed to distinguish the data and the models. Here we consider measures that account for ground state degeneracy and the distributions of excited states, and present evidence that for these new measures neither SQA nor the classical rotor model correlate perfectly with the DW1 experiments. We thus provide evidence that SQA and the classical rotor model, both of which are classically efficient algorithms, do not satisfactorily explain all the DW1 data. A complete model for the DW1 remains an open problem. Using the same criteria we find that, on the other hand, SQA and the classical rotor model correlate closely with each other. To explain this we show that the rotor model can be derived as the semiclassical limit of the spin-coherent states path integral. We also find differences in which set of ground states is found by each method, though this feature is sensitive to calibration errors of the DW1 device and to simulation parameters.

Conclusions

Google will build their own Quantum computer hardware with fault tolerance at Quantum Artificial Intelligence Lab

The Quantum Artificial Intelligence team at Google is launching a hardware initiative to design and build new quantum information processors based on superconducting electronics.

John Martinis and his team at UC Santa Barbara will join Google in this initiative. John and his group have made great strides in building superconducting quantum electronic components of very high fidelity. John was recently awarded the London Prize recognizing him for his pioneering advances in quantum control and quantum information processing.

Superconducting circuits at the surface code threshold for fault tolerance

John Martinis and his team published in the Journal Nature - Superconducting quantum circuits at the surface code threshold for fault tolerance

The superconducting quantum circuit with five Xmon qubits (cross-shaped devices) placed in a linear array. The quantum device shows logic gates with fidelities at the surface code threshold for fault tolerance. (Photo credit: Erik Lucero.)

With an integrated hardware group the Quantum AI team will now be able to implement and test new designs for quantum optimization and inference processors based on recent theoretical insights as well as our learnings from the D-Wave quantum annealing architecture. We will continue to collaborate with D-Wave scientists and to experiment with the “Vesuvius” machine at NASA Ames which will be upgraded to a 1000 qubit “Washington” processor.

Quantum annealing correction for random Ising problems

The performance of a quantum annealer on hard random Ising optimization problems can be substantially improved using quantum annealing correction (QAC). Our error correction strategy is tailored to the D-Wave Two device. They find that QAC provides a statistically significant enhancement in the performance of the device over a classical repetition code, improving as a function of problem size as well as hardness. Moreover, QAC provides a mechanism for overcoming the precision limit of the device, in addition to correcting calibration errors. Performance is robust even to missing qubits. We present evidence for a constructive role played by quantum effects in our experiments by contrasting the experimental results with the predictions of a classical model of the device. Our work demonstrates the importance of error correction in appropriately determining the performance of quantum annealers.

Arxiv - Quantum annealing correction for random Ising problems

Quantum Machine Learning Google Tech Talk

Seth Lloyd visited the Quantum AI Lab at Google LA to give a tech talk on "Quantum Machine Learning." This talk took place on January 29, 2014.

Speaker Info:

Seth Lloyd is one of pioneers in the quantum information science with several seminal contributions to quantum computing, quantum communication, and quantum control. He developed the first quantum algorithms for efficient simulation of many-body systems at the quantum scale. He has also introduced the first realizable model for quantum computation and is working with a variety of groups to construct and operate quantum computers and quantum communication systems. Dr. Lloyd is the author of over a hundred and fifty scientific papers, and of `Programming the Universe,' (Knopf, 2004). He is currently professor of quantum-mechanical engineering at MIT.

Abstract:

Machine learning algorithms find patterns in big data sets. This talk presents quantum machine learning algorithms that give exponential speed-ups over their best existing classical counterparts. The algorithms work by mapping the data set into a quantum state (big quantum data) that contains the data in quantum superposition. Quantum coherence is then used to reveal patterns in the data. The quantum algorithms scale as the logarithm of the size of the database.

At the bottom of quantum mechanics are vectors.

Dwave Systems has government, commercial and intelligence customers lining up for quantum computers

D-Wave deployed what was considered the first commercial quantum computer in 2011. A handful of D-Wave’s quantum computers are now being used by Google, NASA and Lockheed Martin for artificial intelligence, image recognition and machine learning.

D-Wave now has a pipeline of government, commercial and intelligence customers waiting for the company’s faster quantum computers, which will start rolling out later this year, said CEO Vern Brownell.

The company will release faster processors over the next two years that will be central to the new quantum computers, Brownell said. The company currently offers the D-Wave Two, which financial analyst firm Sterne Agee in March estimated had a list price “north of $10 million.”

D-Wave last week received US$28 million in funding from new and existing investors, including Goldman Sachs and BDC Capital. The investment will be used to boost internal software development efforts, but there is room for more funding, Brownell said. The goal is to take the company public in a few years, Brownell said.

D-Wave has 1,152-qubit chips in its lab right now, and hopes to double that to a 2,000-qubit processor next year.

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H and H has been very supportive of D-Wave since the beginning and are cautiously optimistic about its long-term success. Alexei A. Andreev has served on the D-Wave board of directors since the company’s early days and is proud of what they have achieved. The jury is still out on the scaling behavior of D-Wave Adiabatic Quantum Computing (AQC) chips, yet the number of technical breakthroughs delivered by the company since we invested in D-Wave in 2005 has been staggering. From the very beginning, it was clearly an outlier deal — an incredibly risky endeavor that, if it succeeded, could fundamentally change computing and provide disproportional return to its investors.

Researchers find 509 qubit Dwave System performs as well or better than simulated quantum annealing

Arxiv - Quantum Optimization of Fully-Connected Spin Glasses

The Sherrington-Kirkpatrick model with random couplings is programmed on the D-Wave Two annealer featuring 509 qubits interacting on a Chimera-type graph. The performance of the optimizer compares and correlates to simulated annealing. When considering the effect of the static noise, which degrades the performance of the annealer, one can estimate an improvement on the comparative scaling of the two methods in favor of the D-Wave machine. The optimal choice of parameters of the embedding on the Chimera graph is shown to be associated to the emergence of the spin-glass critical temperature of the embedded problem.

14 page paper

Arxiv - A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems

Diagnosing the minimal set of faults capable of explaining a set of given observations, e.g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques. We present the mapping of this combinatorial problem to quadratic unconstrained binary optimization (QUBO), and the experimental results of instances embedded onto a quantum annealing device with 509 quantum bits. Besides being the first time a quantum approach has been proposed for problems in the advanced diagnostics community, to the best of our knowledge this work is also the first research utilizing the route Problem → QUBO → Direct embedding into quantum hardware, where we are able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization. We discuss some of the programmability challenges in the current generation of the quantum device as well as a few possible ways to extend this work to more complex arbitrary network graphs.

Dwave Systems and Google are trying to find Quantum Supremacy

Dwave Systems has known for about three years that is substantial quantum entanglement in their systems. They have been able to show this entanglement on much larger systems [than a recently publish 8 qubit result] and obtained similar results. So Dwave Systems knows there is substantial entanglement in these types of systems. Right now they are trying to characterize how to use it to gain what Google calls ‘quantum supremacy’. Geordie Rose has commented that there has been very good progress on that [finding quantum supremacy]…

Not even the people who built the 512 qubit and 1024 qubit Dwave Quantum computing systems know exactly how it works and what it can do. That’s what Google's Hartmut Neven is trying to figure out, sitting in his lab, week in, week out, patiently learning to talk to the D-Wave. If he can figure out the puzzle—what this box can do that nothing else can, and how—then boom. “It’s what we call ‘quantum supremacy,’” he says. “Essentially, something that cannot be matched anymore by classical machines.” It would be, in short, a new computer age.

More published evidence of Quantum Entanglement with Dwave Systems Qubits

A new paper published today in Phys Rev X demonstrates eight qubit entanglement in a D-Wave processor, which is a world record for solid state qubits. This is an exceptional paper with an important result. The picture below measures a quantity that, if negative, verifies entanglement. The quantity s is the time — the quantum annealing procedure goes from the left to the right, with entanglement maximized near the area where the energy gap is smallest.

Upper limit of the quantity Tr[WABρ] versus s for several bipartitions A-B of the eight-qubit system. When this quantity is less than 0, the system is entangled with respect to this bipartition. The solid dots show the upper limit on Tr[WABρ] for the median bipartition. The open dots above and below these are derived from the two bipartitions that give the highest and lowest upper limits on Tr[WABρ], respectively. For the points at s greater than 0.3, the measurements of P1 and P2 do not constrain ρ enough to certify entanglement.

Quantum algorithms hold the promise of helping to solve a broad range of problems that are simply intractable with classical algorithms. The advantage of quantum calculations stems from exploiting the strange and nonintuitive properties of quantum systems: tunneling, superposition, quantum coherence, and entanglement. Building a general-purpose quantum computer, however, is extremely challenging; a more scalable and feasible approach may involve implementing a single, simpler quantum algorithm, such as quantum annealing. It is critical to demonstrate that such a scalable processor has access to quantum-mechanical resources such as coherence and entanglement. We build a processor based on quantum annealing and verify that specific two- and eight-qubit systems become entangled, a necessary and significant step in developing quantum annealing into a viable quantum-computing technology.

We run quantum annealing on a processor chip composed of magnetically coupled superconducting flux qubits. The chip is mounted on the mixing chamber of a dilution refrigerator held at 12.5 mK. We use qubit tunneling spectroscopy to infer nonclassical correlations in two- and eight-qubit systems based on eigenspectra and level occupations, effects that persist even at thermal equilibrium. Our measurements of spectral lines are dominated by the noise of the qubit tunneling spectroscopy probe, however, and we expect that follow-up experiments with improved probes will enable larger systems of qubits to be studied.

Our work provides promise that quantum annealing is a viable approach to realizing quantum-computing technologies. Moreover, our technique represents an effective way of studying quantum behavior in a practical processor, helping us to further understand the capabilities of quantum algorithms.