Quantinuum today announced a significant upgrade to its ion trap quantum computer, H1-1, which now has 20 qubits – up from 12 qubits – and features all-to-all connectivity. At the same time, researchers from JPMorgan Chase released a paper showcasing work performed on the upgraded H1-1 demonstrating the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware.
“With these upgrades, developers are able to run more complex calculations than they could before without sacrificing performance,” said Tony Uttley, Quantinuum’s president and chief operating officer in the official announcement. “This upgrade is yet another example of our unique business model of continuously upgrading our systems, even after they are in commercial use, to provide the best performance to our users.”
Upgrades made to the H1-1 machine included:
- Increasing the number of fully connected qubits from 12 to 20 while simultaneously preserving its low two-qubit gate errors (typical performance fidelities of 99.7 percent with fidelities as high as 99.8 percent) and critical features such as mid-circuit measurement, qubit reuse, quantum conditional logic and all-to-all connectivity.
- Increasing the number of gate zones from three to five, enabling the H1-1 to complete more quantum operations simultaneously and allowing increased parallelization in circuit execution.
The second version of the System Model H1, the H1-2, is scheduled to undergo similar upgrades later this year. So far, there are few details about the forthcoming H1-2 system upgrade.
Utley said, “We are adding qubits and maintaining fidelity without compromising any features, which is absolutely essential as we scale to our future H-Series generations.”
It is, of course, still unclear which of the many competing qubit technologies (trapped ion, superconducting, photonic, neutral atoms, et al.) will predominate over time. Ion trap technology has distinct strengths – identical qubits, long coherence, among them – and some drawbacks. So far, there are no perfect qubits.
The JPMorgan Chase* work issued in a arXiv preprint (Constrained Quantum Optimization for Extractive Summarization (ES) on a Trapped-ion Quantum Computer) is more evidence of steady advance in trapped ion technology.
“The quantum computing team at JPMorgan Chase has been using Quantinuum’s quantum computer to run experiments that use mid-circuit measurement and reuse and quantum conditional logic, taking advantage of the computer’s very high quantum volume,” said Marco Pistoia, distinguished engineer and head of Quantum Computing and Communication Research in the bank. “We used the 20 qubits of the H1-1 computer on a quantum Natural Language Processing algorithm for extractive text summarization. The results were almost identical to the reference values computed with a noiseless simulator, validating the computer’s high fidelity.”
It was an interesting exercise choice – text summarization – is perhaps not what most envision as an application for quantum computing. However, it turns out ES is an excellent example of a difficult optimization problem and has, in fact, been used run on several different quantum computers (IBM, Rigetti, IonQ) and to test system performance and some of those efforts are discussed in the text.
The abstract nicely summarizes the JPMorgan Chase work:
“Realizing the potential of near-term quantum computers to solve industry-relevant constrained- optimization problems is a promising path to quantum advantage. In this work, we consider the extractive summarization constrained-optimization problem and demonstrate the largest-to-date execution of a quantum optimization algorithm that natively preserves constraints on quantum hardware. We report results with the Quantum Alternating Operator Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA) on the Quantinuum H1-1 trapped-ion quantum computer.
“We successfully execute XY-QAOA circuits that restrict the quantum evolution to the in-constraint subspace, using up to 765 two-qubit gates with a two-qubit gate depth of up to 159, and all 20 qubits of the H1-1 device. We demonstrate the necessity of directly encoding the constraints into the quantum circuit by showing the trade-off between the in-constraint probability and the quality of the solution that is implicit if unconstrained quantum optimization methods are used. We show that this trade-off makes choosing good parameters difficult in general. We compare XY- QAOA to the Layer Variational Quantum Eigensolver algorithm, which has a highly expressive constant-depth circuit, and the Quantum Approximate Optimization Algorithm. Our experimental results demonstrate that the rapid hardware and algorithmic progress is enabling the solution of constrained-optimization problems on quantum hardware.”
Shown below is a table from the paper with results from similar experiments as well as the JPMorgan exercise.
The researchers write, “ES is a particularly interesting problem to consider since it has challenges that are similar to those of many other industrially-relevant use cases. First, it is constrained, making it necessary to either restrict the quantum evolution to the corresponding subspace, or introduce large penalty terms into the formulation. Second, it lacks simple structures, such as symmetries. Third, unlike commonly considered toy problems, such as Max-Cut, the coefficients in its objective are not necessarily integers, which can make the optimization of quantum algorithm parameters hard.”
Very broadly, think of ES as text mining/summarizing. It would have been interesting, just from a curiosity perspective, to see examples of the actual excerpted text (ES) in this exercise. That said, the value of this experiment is performing optimization on an NP-hard problem on a quantum computer. For this experiment, the researchers used “articles from the CNN/DailyMail dataset. This dataset contains just over 300k unique news articles written by journalists at CNN and the Daily Mail in English.”
In their conclusion, the authors note more work is needed, “We additionally show the necessity of embedding the constraints directly into the quantum circuit being used. If the circuit does not preserve the constraints, the in-constraint probability and the quality of the in-constraint solution have to be traded-off against each other. This trade-off is hard to do in general. This observation further motivates our investigation of XY-QAOA on and gives additional weight to our results. At the same time, we show that further advances are needed to reduce the hardware requirements of implementing such circuits and improve the fidelities of the hardware.”
As always with these kinds of technologies, it is best to read the paper directly.
Besides providing direct access to its systems, Quantinuum also provides access to its trapped ion quantum computers H1-1 and H1-2 as well as the H1 Emulators through Microsoft’s Azure Quantum.
“The continuous upgrading of Quantinuum’s systems has been a great benefit to Microsoft customers. Microsoft is pleased to offer the new capabilities of H1-1 with 20 qubits to customers who access Quantinuum H-systems through Microsoft Azure Quantum and our Azure Quantum Credits program which provides free access to quantum hardware to customers,” said Fabrice Frachon, Azure Quantum Principal Program Manager. For more information about the newly upgraded H1-1, go to http://quantinuum.com/n20.
Link to JPMorgan paper, https://arxiv.org/abs/2206.06290
* This paper was prepared for information purposes by the Future Lab for Applied Research and Engineering (FLARE) group of JPMorgan Chase Bank, N.A. This paper is not a product of the Research Department of JPMorgan Chase Bank, N.A. or its affiliates.