In March 2018, Google unveiled Bristlecone, a new quantum computing chip with 72 quantum bits, or qubits—the fundamental units of computation in a quantum machine beating the previous record holder of 50-qubit processor by IBM. The goal of the Google Quantum AI lab is to build a quantum computer that can be used to solve real-world problems. Our strategy is to explore near-term applications using systems that are forward compatible to a large-scale universal error-corrected quantum computer. In order for a quantum processor to be able to run algorithms beyond the scope of classical simulations, it requires not only a large number of qubits. Crucially, the processor must also have low error rates on readout and logical operations, such as single and two-qubit gates.
John Martinis, who heads Google’s effort, said his team still needs to do more testing, but he thinks the new chip can achieve “quantum supremacy. That’s the point at which a quantum computer can do calculations beyond the reach of today’s fastest supercomputers. In Oct 2019, Researchers in UC Santa Barbara/Google scientist John Martinis’ group have made good on their claim to quantum supremacy. Using 53 entangled quantum bits (“qubits”), their Sycamore computer has taken on — and solved — a problem considered intractable for classical computers.
“A computation that would take 10,000 years on a classical supercomputer took 200 seconds on our quantum computer,” said Brooks Foxen, a graduate student researcher in the Martinis Group. “It is likely that the classical simulation time, currently estimated at 10,000 years, will be reduced by improved classical hardware and algorithms, but, since we are currently 1.5 trillion times faster, we feel comfortable laying claim to this achievement.” The feat is outlined in a paper in the journal Nature. The task itself, which involved executing a randomly chosen sequence of instructions, does not have any particular practical uses. But experts say the achievement is still significant as a demonstration of the future promise of quantum computing.
Not everyone agrees that Google’s announcement represents true quantum supremacy. Computer scientists at IBM have countered that their most powerful supercomputer, called Summit, could complete the same task in 2.5 days rather than 10,000 years. Still, Google’s success is a noteworthy steppingstone on what will probably be a long and winding road to quantum supremacy, said John Preskill, a theoretical physicist at Caltech, who coined the phrase, to describe the point at which quantum computers can do things that classical computers simply can’t.
John Preskill, had published a paper in which he said quantum computing was about to enter a phase he called NISQ, or “noisy intermediate stage quantum,” where machines will have 50 to a few hundred qubits. “‘Noisy,’” he wrote, “means that we’ll have imperfect control over those qubits; the noise will place serious limitations on what quantum devices can achieve in the near term.”
Preskill said he’s still convinced quantum computers will have a transformative effect on society, but for that transformation, quantum computers should have more qubits, less noise. That will make the quantum computer more powerful. Presumably, both of these will continue to happen. “We think in quantum chemistry there will be a big impact, which could be important in agriculture and human health. It could help with the development of new pharmaceuticals, new energy sources, new ways to collect solar power, and new materials.”
Google Quantum Computer and D-Wave quantum Computer
Canadian firm D-Wave has already released the new quantum computer that will be able to handle some 2,000 quantum bits (qubits), roughly double the usable number found in the processor in the existing D-Wave 2X system, and be capable of solving certain problems 1,000x faster than its predecessor.
D-Wave Two is not a universal quantum computer, It has been designed specifically to perform a process called “quantum annealing”, which is a technique for finding the global minimum of a complicated mathematical expressions hence expected to be capable of solving optimization and sorting problems exponentially faster than a classical computer. D-Wave approach is that of Analogue quantum computation, that consists of a continuous dynamics to get to the optimal solution of the problem. This dynamics can be slow, as is the case of adiabatic quantum computing based on so-called quantum annealing.
While this method works fairly well for some problems, it is not the one-size-fits-all quantum computation technique that many scientists hope to create. Adiabatic quantum computing suffers from errors and noise because the process does not allow for error correction to take place during a computation. This becomes a major problem when the system is scaled up and errors accumulate. Google also earlier like D-Wave focussed on “quantum annealing” – used to build an “adiabatic” quantum computer that can solve a specific problem which involves finding the global minimum value of a highly complex function.
Google researchers have implemented the digitized adiabatic quantum computing, that combines the generality of the adiabatic algorithm with the universality of the digital approach, using a superconducting circuit with nine qubits. In this pioneering experiment superconducting quantum bits were used to digitize an analogue quantum computer in a way similar to what is done with communication signals in conventional technologies.
Adiabatic quantum computing suffers from errors and noise because the process does not allow for error correction to take place during a computation. This becomes a major problem when the system is scaled up and errors accumulate. In contrast, a key component of many of the universal quantum computing architectures being developed is that their digital logic gates can be made fault-tolerant and error correction can take place while a calculation is being processed.
In contrast, digital quantum computation splits up the problem to be resolved in terms of quantum logic gates in a way similar to that of a conventional computer. A key component of many of the universal quantum computing architectures being developed is that their digital logic gates can be made fault-tolerant and error correction can take place while a calculation is being processed. This strategy will enable optimization problems to be universally solvable; they are useful in fields as general as finance, and also in the design of new materials and products for the pharmaceutical industry.
Google’s universal QPU can solve a wider range of problems than D-Wave’s QPU (in it’s current implementation) if they can solve their decoherence problem. Bristlecone is a scaled up version of a 9-qubit Google design that has failed to yield acceptable error rates for a commercially viable quantum system. In real-world settings, quantum processors must have a two-qubit error rate of less than 0.5 percent. According to Google, its best result has been a 0.6 percent error rate using its much smaller 9-qubit hardware.
Google combines two approaches to quantum computing
“Another problem with adiabatic quantum computing is the classical nature of the interactions – which puts a low limit on the number of other qubits that a qubit can interact with. For a quick computation, you would ideally want multiple interactions simultaneously taking place between all of the qubits. But in a complex computation, it would be nearly impossible to accurately keep track of these interactions. However, reducing the connectivity has a major impact on the system’s computational abilities.” “A complementary approach is digital quantum computing, which enables the construction of arbitrary interactions and is compatible with error correction but uses quantum circuit algorithms that are problem-specific.”
John Martinis, Rami Barends and colleagues at Google’s research laboratories in Santa Barbara, California, together with physicists at the University of California, Santa Barbara and the University of the Basque Country in Bilbao, have combined the advantages of both approaches by implementing digitized adiabatic quantum computing in a superconducting system. By digitizing an adiabatic quantum computation, the team has a greater degree of control over the interactions between qubits, and they can also correct for errors while a computation is executed.
In the current work, the Google researchers have adapted their previously built superconducting nine-qubit chip, where interactions are controlled by connected logic gates that encode a problem. The spin system is formed by a superconducting circuit with nine qubits. The team simulated a row of spin-coupled magnetic atoms in a chain. The atoms can have an aligned (ferromagnetic) or anti-aligned (anti-ferromagnetic).
The qubits are the cross-shaped structures, patterned out of an Al layer on top of a sapphire substrate, and arranged in a linear chain. Each qubit is capacitively coupled to its nearest neighbours, and can be individually controlled and measured. The researchers address each qubit individually via current pulses that tune into the inherent resonant frequency of their qubits, which have variable frequencies between 4 GHz and 5.5 GHz. Crucially, by tuning the frequencies of the qubits we can implement a tunable controlled-phase entangling gate, which together with the single qubit gates forms our digitized approach.
“In our architecture we can steer this frequency, much like you would tune a radio to a broadcast,” says Barends. He explains that they can tune the frequency of one qubit to that of another. “By moving qubit frequencies to or away from each other, interactions can be turned on or off. The exchange of quantum information resembles a relay race, where the baton can be handed down when the runners meet,” he adds. The team can even tune a qubit so that it is simultaneously in a superposition of being aligned and anti-aligned.
Barends tells physicsworld.com that “as a demonstration, we have implemented non-stoquastic interactions, something that is not possible with present-day analogue systems. This is important because problems that involve interacting electrons, like in quantum chemistry, are non-stoquastic.” “This demonstration of digitized quantum adiabatic computing in the solid state opens a path to solving complex problems, and we hope it will motivate further research into the efficient synthesis of adiabatic algorithms, on small-scale systems with noise as well as future large-scale quantum computers with error correction,” write the authors.
Google’s New Quantum Processor plans to prove quantum supermacy
The goal of the Google Quantum AI lab is to build a quantum computer that can be used to solve real-world problems. Our strategy is to explore near-term applications using systems that are forward compatible to a large-scale universal error-corrected quantum computer. In order for a quantum processor to be able to run algorithms beyond the scope of classical simulations, it requires not only a large number of qubits. Crucially, the processor must also have low error rates on readout and logical operations, such as single and two-qubit gates.
Bristlecone is Google’s newest quantum processor (left). The purpose of this gate-based superconducting system is to provide a testbed for research into system error rates and scalability of our qubit technology, as well as applications in quantum simulation, optimization, and machine learning.”
The guiding design principle for this device is to preserve the underlying physics of our previous 9-qubit linear array technology which demonstrated low error rates for readout (1%), single-qubit gates (0.1%) and most importantly two-qubit gates (0.6%) as our best result. This device uses the same scheme for coupling, control, and readout, but is scaled to a square array of 72 qubits. Operating a device such as Bristlecone at low system error requires harmony between a full stack of technology ranging from software and control electronics to the processor itself. Getting this right requires careful systems engineering over several iterations.
We chose a device of this size to be able to demonstrate quantum supremacy in the future, investigate first and second order error-correction using the surface code, and to facilitate quantum algorithm development on actual hardware. If a quantum processor can be operated with low enough error, it would be able to outperform a classical supercomputer on a well-defined computer science problem, an achievement known as quantum supremacy. Google has also developed a benchmarking tool to quantify a quantum processor’s capabilities . We can assign a single system error by applying random quantum circuits to the device and checking the sampled output distribution against a classical simulation.
“Although no one has achieved this goal yet, we calculate quantum supremacy can be comfortably demonstrated with 49 qubits, a circuit depth exceeding 40, and a two-qubit error below 0.5%. We believe the experimental demonstration of a quantum processor outperforming a supercomputer would be a watershed moment for our field, and remains one of our key objectives.”
Google has enlisted NASA to help it prove quantum supremacy. The agreement, signed in July 2018, calls on NASA to “analyze results from quantum circuits run on Google quantum processors, and … provide comparisons with classical simulation to both support Google in validating its hardware and establish a baseline for quantum supremacy.” In May, researchers with Alibaba’s Data Infrastructure and Search Technology Division published a paper suggesting that classical computers running simulations could match its performance, and that quantum chips with lower error rates might be needed.
The Researchers from the Quantum Artificial Intelligence Laboratory (QuAIL) at NASA’s Ames Research Center in Silicon Valley will connect to Bristlecone online, via Google’s Cloud API service. Google will also share current software that allows classical computers to simulate quantum circuits, so that NASA can develop and improve upon it. Together, the two organizations will work out how to map “a diverse array of optimization and sampling problems” to Bristlecone’s gate-model quantum computing system. NASA will code the software necessary to run those simulations on its petaflop-scale Pleiades supercomputer, also located at Ames. Pleiades is NASA’s most powerful supercomputer, currently ranked in the top 25 worldwide.
If things do not go as planned, Google’s agreement has a five-year term within which “NASA will provide further mappings, improved circuit simulation techniques, more efficient compilations [and] results from circuit simulations.” Google will give QuAIL access to its quantum processor and software until at least 2023.
Google achieves Quantum Supremacy
“The algorithm was chosen to emphasize the strengths of the quantum computer by leveraging the natural dynamics of the device,” said Ben Chiaro, another graduate student researcher in the Martinis Group. That is, the researchers wanted to test the computer’s ability to hold and rapidly manipulate a vast amount of complex, unstructured data.
“We basically wanted to produce an entangled state involving all of our qubits as quickly as we can,” Foxen said, “and so we settled on a sequence of operations that produced a complicated superposition state that, when measured, returns bitstring with a probability determined by the specific sequence of operations used to prepare that particular superposition. The exercise, which was to verify that the circuit’s output correspond to the sequence used to prepare the state, sampled the quantum circuit a million times in just a few minutes, exploring all possibilities — before the system could lose its quantum coherence.
“We performed a fixed set of operations that entangles 53 qubits into a complex superposition state,” Chiaro explained. “This superposition state encodes the probability distribution. For the quantum computer, preparing this superposition state is accomplished by applying a sequence of tens of control pulses to each qubit in a matter of microseconds. We can prepare and then sample from this distribution by measuring the qubits a million times in 200 seconds.”
“For classical computers, it is much more difficult to compute the outcome of these operations because it requires computing the probability of being in any one of the 2^53 possible states, where the 53 comes from the number of qubits — the exponential scaling is why people are interested in quantum computing to begin with,” Foxen said. “This is done by matrix multiplication, which is expensive for classical computers as the matrices become large.”
According to the new paper, the researchers used a method called cross-entropy benchmarking to compare the quantum circuit’s output (a “bitstring”) to its “corresponding ideal probability computed via simulation on a classical computer” to ascertain that the quantum computer was working correctly.
“We made a lot of design choices in the development of our processor that are really advantageous,” said Chiaro. Among these advantages, he said, are the ability to experimentally tune the parameters of the individual qubits as well as their interactions.
“It’s kind of a continuous improvement mindset,” Foxen said. “There are always projects in the works.” In the near term, further improvements to these “noisy” qubits may enable the simulation of interesting phenomena in quantum mechanics, such as thermalization, or the vast amount of possibility in the realms of materials and chemistry.
In the long term, however, the scientists are always looking to improve coherence times, or, at the other end, to detect and fix errors, which would take many additional qubits per qubit being checked. These efforts have been running parallel to the design and build of the quantum computer itself, and ensure the researchers have a lot of work before hitting their next milestone.
While the experiment was chosen as a proof-of-concept for the computer, the research has resulted in a very real and valuable tool: a certified random number generator. Useful in a variety of fields, random numbers can ensure that encrypted keys can’t be guessed, or that a sample from a larger population is truly representative, leading to optimal solutions for complex problems and more robust machine learning applications. The speed with which the quantum circuit can produce its randomized bit string is so great that there is no time to analyze and “cheat” the system.
“Quantum mechanical states do things that go beyond our day-to-day experience and so have the potential to provide capabilities and application that would otherwise be unattainable,” commented Joe Incandela, UC Santa Barbara’s vice chancellor for research. “The team has demonstrated the ability to reliably create and repeatedly sample complicated quantum states involving 53 entangled elements to carry out an exercise that would take millennia to do with a classical supercomputer. This is a major accomplishment. We are at the threshold of a new era of knowledge acquisition.”