Q-Roundup: Google on Optimizing Circuits; St. Jude Uses GenAI; Hunting Majorana; Global Movers

By John Russell

February 27, 2024

Last week, a Google-led team reported developing a new tool – AlphaTensor Quantum – based on deep reinforcement learning (DRL) to better optimize circuits. A week earlier a team working with St. Jude Children’s Hospital used GenAI in a demonstration hybrid classical-quantum project to identify new cancer-fighting molecules. Both efforts represent significant steps forward and the growing incorporation of AI techniques in quantum computing.

There’s yet another fascinating paper recently out from Harvard and Quantinuum in which researchers report being the first to generate non-abelian topological order. They did so using Quantinuum’s H2 trapped ion quantum system.

Meanwhile, the global race in quantum computing keeps heating up:

  • Russia reportedly plans to have a 50-qubit device up and running this year.
  • South Korea plans to launch cloud-based quantum services later this year.
  • In Europe, a report by the EuroHPC JU-backed HPCQS (high performance computer-quantum simulator) argues that quantum computers can help tame the world energy consumption.

With respect to the last item, the HPCQS report, which is being coordinated by Jülich Supercomputing Centre (JSC), takes note of big datacenters and big machines. This excerpt is from the report:

“In 2020, IT represented 11% of the global electricity consumption (Puebla et al., 2020). An analysis by Huawei Technologies shows that it can increase to 51% in 2050 (Andrae & Edler, 2015), that this electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030, including, but not limited, to data center computation and storage (also called HPC centers or supercomputers). Supercomputers can consume the same amount of electricity as a town. For instance, the Frontier supercomputer, fabricated by Hewlett Packard and hosted at the DoE Oak Ridge Laboratory in Tennessee, USA, uses 504 MWh on average daily, summing up the energy consumed by around 17 thousand average homes in the U.S. daily (EIA, 2023). And this is only one data center.”


Let’s start with AlphaTensor Quantum

Google, Quantinuum, IIU Develop AlphaTensor Quantum for Circuit Optimization

Optimizing quantum circuits – both gate type and gate count – is a critical step in making effective use of quantum computers. Researchers from Google’s DeepMind, Quantinuum, and the Informatics Institute, University of Amsterdam, have developed a new approach – called AlphaTensor-Quantum – that minimizes the number of T gates required for circuits.

Their approach is detailed in a paper issued last week. “We envision this approach can significantly accelerate discoveries in quantum computation as it saves the numerous hours of research invested in the design of optimized circuits,” write the researchers.

Here’s the abstract:

“A key challenge in realizing fault-tolerant quantum computers is circuit optimization. Focusing on the most expensive gates in fault-tolerant quantum computation (namely, the T gates), we address the problem of T-count optimization, i.e., minimizing the number of T gates that are needed to implement a given circuit. To achieve this, we develop AlphaTensor-Quantum, a method based on deep reinforcement learning that exploits the relationship between optimizing T-count and tensor decomposition. Unlike existing methods for T-count optimization, AlphaTensor-Quantum can incorporate domain-specific knowledge about quantum computation and leverage gadgets, which significantly reduces the T-count of the optimized circuits.

“AlphaTensor-Quantum outperforms the existing methods for T-count optimization on a set of arithmetic benchmarks (even when compared without making use of gadgets). Remarkably, it discovers an efficient algorithm akin to Karatsuba’s method for multiplication in finite fields. AlphaTensor-Quantum also finds the best human-designed solutions for relevant arithmetic computations used in Shor’s algorithm and for quantum chemistry simulation, thus demonstrating it can save hundreds of hours of research by optimizing relevant quantum circuits in a fully automated way.”

As always, the paper is best read directly.

The researchers report, “We show that AlphaTensor-Quantum is a powerful method for finding efficient quantum circuits. On a benchmark of arithmetic primitives, AlphaTensor-Quantum outperforms all existing methods for T-count optimization, especially when allowed to leverage domain knowledge. For the relevant operation of multiplication in finite fields, which has applications in cryptography, AlphaTensor-Quantum finds an efficient quantum algorithm with the same complexity as the classical Karatsuba’s method.”

They also note, “As quantum computations are reversible by nature, naive translations of classical algorithms commonly introduce overhead. AlphaTensor-Quantum thus finds the most efficient quantum algorithm for multiplication on finite fields reported to date. We also optimize quantum primitives for other relevant problems, ranging from arithmetic computations used, e.g., in Shor’s algorithm, to Hamiltonian simulation in quantum chemistry, e.g., FeMoco simulation. Here, AlphaTensor-Quantum recovers the best known hand-designed solutions, demonstrating it can effectively optimize circuits of interest in a fully automated way.”

Link to paper, https://arxiv.org/abs/2402.14396

Link to Quantinuum post, https://www.linkedin.com/posts/quantinuumqc_learn-more-here-activity-7166782016200384512-udjB/

GenAI on Quantum Device Advances Cancer Research

Using computers to shortcut the search for viable drugs has a long history. While the technology advances have been steady and impressive, the payoff in real drugs has been somewhat erratic. Recently, researchers from ZapataAI, St. Jude Children’s Research Hospital, Insilico Medicine, and the University of Toronto took another step forward demonstrating the first use of generative AI (GenAI) in a hybrid classical-quantum approach to find novel KRAS inhibitors.

Without doubt, drug R&D is a messy, difficult-to-predict process that can cost $ billions and take a decade or more. In their paper issued earlier this month, the researchers provide a nice summary of the discovery process:

“The drug discovery journey commences with identifying a critical target, usually a protein or enzyme integral to a disease’s pathophysiology. Following this step, researchers employ many techniques, notably virtual screening, to design and rigorously assess potential drug candidates creatively. These candidates are meticulously evaluated for their proficiency in engaging with and modulating the target, propelling the pursuit of therapeutic innovations. Concurrently, generative modeling is emerging as a transformative technology in molecule design. Generative models utilize machine learning techniques to comprehend the underlying distribution of atoms and bonds in a specified dataset. Subsequently, these models are employed to construct molecules with predefined properties, a process known as inverse molecular design. A promising aspect of these models is their ability to navigate the vast chemical space, proposing interesting molecules within the challenging realm of 1060 drug-like molecules.”

Using GenAI on quantum computers in a hybrid approach that uses both classical and quantum computers is new. In this instance, generative models running on classical hardware, quantum hardware (specifically, a 16-qubit IBM device), and simulated quantum hardware generated one million drug candidates each, which were then filtered algorithmically and by humans. The resulting 15 molecules were then synthesized and tested through cell-based assays. The two molecules generated by the quantum-enhanced generative model were distinct from existing KRAS inhibitors and showed a superior binding affinity over the molecules generated by purely classical models.

Here’s the abstract:

“[W]e introduce a quantum-classical generative model that seamlessly integrates the computational power of quantum algorithms trained on a 16-qubit IBM quantum computer with the established reliability of classical methods for designing small molecules. Our hybrid generative model was applied to designing new KRAS inhibitors, a crucial target in cancer therapy. We synthesized 15 promising molecules during our investigation and subjected them to experimental testing to assess their ability to engage with the target. Notably, among these candidates, two molecules, ISM061-018-2 and ISM061-22, each featuring unique scaffolds, stood out by demonstrating effective engagement with KRAS. ISM061-018-2 was identified as a broad-spectrum KRAS inhibitor, exhibiting a binding affinity to KRAS-G12D at 1.4μM

“Concurrently, ISM061-22 exhibited specific mutant selectivity, displaying heightened activity against KRAS G12R and Q61H mutants. To our knowledge, this work shows for the first time the use of a quantum-generative model to yield experimentally confirmed biological hits, showcasing the practical potential of quantum-assisted drug discovery to produce viable therapeutics. Moreover, our findings reveal that the efficacy of distribution learning correlates with the number of qubits utilized, underlining the scalability potential of quantum computing resources. Overall, we anticipate our results to be a stepping stone towards developing more advanced quantum generative models in drug discovery.”

The paper is best read directly and the researchers note there is more to do. “While the results showcased are promising, they do not conclusively establish a ’quantum advantage’, defined as achieving results beyond the reach of classical methods within a reasonable timeframe. The modest count of 16 qubits in our hybrid algorithm permits simulation on classical platforms, suggesting that state-of-the-art classical algorithms might match or even exceed the efficacy of our quantum-assisted approach. Hence, a critical future research direction involves comprehensively assessing our hybrid quantum-classical algorithm’s performance compared to its classical equivalents.”

A follow-on study, say the researchers, might include analyzing the “scalability relative to qubit quantity, the intricacies of qubit types and their interconnections, the influence of quantum noise and errors, and how the algorithm measures up against top-tier classical algorithms in terms of success rates and other crucial metrics like the docking scores of ligands.”

Nevertheless, they write, “Our research indicates that current near-term quantum hardware can already be harnessed for practical drug discovery applications, mitigating the need to wait for fully fault-tolerant quantum computers, which may be a decade from fruition. Moreover, since our algorithm uses only 16 qubits within the realm of classical simulation, it shows how quantum computing can catalyze the development of more efficient algorithms for classical hardware.”

Link to paper, https://arxiv.org/abs/2402.08210

Link to ZapataAI announcement, https://www.hpcwire.com/off-the-wire/zapata-ai-collaborates-with-top-researchers-to-outperform-classical-ai-in-cancer-treatment-innovations/

Chasing non-Abelian Quasiparticles with Harvard and Quantinuum

Productive efforts to observe and use topological states for quantum computing based on so-called quasiparticles (e.g. the Majorana) have been on the rise. Most recently, researchers from Harvard and Quantinuum reported in Nature they had produced a non-Abelian topological order using Quantinuum’s H2 trapped ion quantum system, which they say has only been done in theory until now.

In theory, topological qubits would be inherently resistant to error and smooth the path to achieving fault tolerant quantum computing. Phys.org has posted a short account of the work (actually a reprint of a Harvard Gazette article written by Anne Manning). The Nature paper (Non-Abelian topological order and anyons on a trapped-ion processor) was published on Valentine’s Day, perhaps a fitting gift for seekers of these mysterious states of matter.

Here’s the paper’s abstract:

“Non-Abelian topological order is a coveted state of matter with remarkable properties, including quasiparticles that can remember the sequence in which they are exchanged. These anyonic excitations are promising building blocks of fault-tolerant quantum computers. However, despite extensive efforts, non-Abelian topological order and its excitations have remained elusive, unlike the simpler quasiparticles or defects in Abelian topological order. Here we present the realization of non-Abelian topological order in the wavefunction prepared in a quantum processor and demonstrate control of its anyons.

“Using an adaptive circuit on Quantinuum’s H2 trapped-ion quantum processor, we create the ground-state wavefunction of D4 topological order on a kagome lattice of 27 qubits, with fidelity per site exceeding 98.4 per cent. By creating and moving anyons along Borromean rings in spacetime, anyon interferometry detects an intrinsically non-Abelian braiding process. Furthermore, tunnelling non-Abelions around a torus creates all 22 ground states, as well as an excited state with a single anyon—a peculiar feature of non-Abelian topological order. This work illustrates the counterintuitive nature of non-Abelions and enables their study in quantum devices.”

Manning wrote, “The researchers employed some dogged creativity to realize their exotic matter state. Maxing out the capabilities of Quantinuum’s newest H2 processor, the team started with a lattice of 27 trapped ions. They used partial, targeted measurements to sequentially increase the complexity of their quantum system, effectively ending up with an engineered quantum wave function with the exact properties and characteristics of the particles they were after.”

Harvard researcher, Ashvin Vishwanath, is quoted, ”Measurement is the most mysterious aspect of quantum mechanics, leading to famous paradoxes like Schrödinger’s cat and numerous philosophical debates. Here we used measurements as a tool to sculpt the quantum state of interest.”

Link to phys.org post, https://phys.org/news/2024-02-phase-physicists-abelian-anyons-quantum.html

Link to Nature paper, https://www.nature.com/articles/s41586-023-06934-4

Russia and S. Korea QC Expansion; Europe’s Focus on Energy Miserliness

The Russian news agency Tass has reported last week that Russian plans to have a 50-qubit quantum computer operating this year and a 100-qubit system at some point.

Here’s an excerpt from Tass: “A 16-qubit ion-based quantum computer was shown to Russian President Vladimir Putin in 2023, where a molecule calculation algorithm was launched with the use of a cloud platform. It was the most powerful quantum computer in the country at that time.”

The Tass report quotes a senior advisor, “We have developed a 20-qubit quantum computer as part of the roadmap on quantum computations. We implemented it on an ion platform. We also have a 25-qubit computer on a nuclear platform. We have plans [for computers] from 50 to 100 qubits. We will be able to make a 50 [qubit computer] by the end of this year.”

South Korea is also ramping up its efforts. The Chosun Daily reported the implementation plan for major projects under the New Growth 4.0 strategy includes opening a domestically developed 20-qubit quantum computer cloud service to the private sector in the latter half of this year. The goal, according to the report, is to achieve 50 qubits of quantum computer technology by 2026 and to scale up to 1,000 qubits by 2032.

Lastly Europe’s HPCQS has been circulating a white paper (Towards Regenerative Quantum Computing with proven positive sustainability impact) that was also shown at the U.N. Climate Change Conference in Dubai. The document makes a case for quantum computing to become an important means for controlling skyrocketing energy consumption costs related to IT broadly, and large datacenters in particular.

The HPCQS project is coordinated by the Jülich Supercomputing Centre (JSC) and aims to integrate two 100-qubit quantum simulators built by the French start-up PASQAL into the supercomputing infrastructures of JSC and the French supercomputing centre CEA/TGCC.

Here’s an excerpt from the white paper:

“By combining quantum computing with artificial intelligence and big data analytics, quantum technologies can exploit potential synergies to accelerate environmental innovation. This will identify integrated solutions, such as smart grids, sustainable agricultural practices, and circular economic mechanisms, to maximize positive environmental impact. Will quantum advantage arrive with energy consumption advantage? We still don’t know, but what we can say today is that current quantum computers’ electricity usage is orders of magnitude much less than any supercomputer, counting all the different quantum architectures available. Being superconducting qubits the most expensive architecture, they only consume about 25 kW (Ezratty, 2023). That amounts to 600 kWh daily, a thousand times less than the Frontier supercomputer. Much less is the consumption of neutral atom quantum devices, such as PASQAL’s, which amount up to 3 kW. But again, no current quantum computing can address all the problems that Frontier (U.S. Supercomputer) is able to tackle.”

Link to phys.org article, https://phys.org/news/2024-02-doubly-sustainable-quantum.html#google_vignette

Link to HPCQS paper, https://static1.squarespace.com/static/65782d915f32dc08287e5163/t/65797899898c0672833a7ef8/1702459875640/White_Paper_Towards_Regenerative_Sustainable_Impact_Quantum_Computing.pdf

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