hamiltonian-parameters-quantum-simulators

Unlocking Quantum Simulations: Scientists Develop Techniques for Estimating Hamiltonian Parameters in Superconducting Quantum Simulators

Introduction

Scientists from Freie University Berlin, University of Maryland, NIST, Google AI, and Abu Dhabi aimed to estimate the free Hamiltonian parameters of bosonic excitations in superconducting quantum simulators. Their protocols, shared in an arXiv preprint, could enable highly precise quantum simulations that surpass classical computing.

The Call from Google AI

Jens Eisert, the paper's lead author, told "I was attending a conference in Brazil when i got a call from colleagues at the Google AI team."

Challenges in Calibrating Quantum Chips

"While working to calibrate their Sycamore superconducting quantum chip with Hamiltonian learning methods, they encountered substantial difficulties and called for help. With my background in analog quantum simulation and systems identification, I found their request particularly compelling."

Understanding Hamiltonian Learning

Initial Assumptions and Realizations

At first, Eisert assumed that the issue raised by his friends would be simple to address. However, he quickly discovered that it was more complex than expected, as the system's Hamiltonian operator frequencies were not precise enough to determine the unknown Hamiltonian from the available data.

"I invited two brilliant Ph.D. students, Ingo Roth and Dominik Hangleiter, and together, we promptly devised a solution using superresolution techniques--in theory, at least, until the data arrived," said Eisert.

Overcoming Complexities

"It took several more years before we fully understood how to make Hamiltonian learning robust enough for application in large-scale experiments."

"During that time, another Ph.D. student, Jonas Fuksa, joined the team, while the other two had already graduated. Pedram Roushan, the experimental lead of Google AI, remained steadfast and provided exceptional data, which ultimately helped us find a solution to the problem posed in the Zoom call years ago."

Techniques and Innovations in Hamiltonian Learning

Applying Superresolution

To uncover the Hamiltonian dynamics of a superconducting quantum simulator, Eisert and his team utilized several techniques. Initially, they applied superresolution to improve the precision of eigenvalue estimation and accurately determine Hamiltonian frequencies.

Manifold Optimization

They subsequently employed a technique called manifold optimization to retrieve the eigenspaces of the Hamiltonian operator, effectively reconstructing the Hamiltonian. Manifold optimization involves specialized algorithms designed to address complex problems where variables reside on a manifold (a smooth and curved space) instead of in conventional Euclidean space.

"To achieve reliable estimates, we integrated several concepts," Eisert explained.

The TensorEsprit Approach

"Understanding the processes of switching on and off was crucial, as these processes are neither perfect nor instantaneous (and not even unitary). Attempting to fit a Hamiltonian evolution that is partially non-Hamiltonian leads to significant complications. Ultimately, we developed new signal processing methods, termed TensorEsprit, which enabled robust recovery even for large system sizes."

Results and Future Implications

Precision and scalability of the Techniques

The researchers introduce a new method for implementing super-resolution in their paper, which they have termed TensorEsprit. By combining this method with a manifold optimization strategy, they effectively identified the Hamiltonian parameters for as many as 14 coupled superconducting qubits across two Sycamore processors.

Future Studies and Applications

"During the initial phase, grasping the overall importance of Hamiltonian learning methods was crucial," stated Eisert.

"One can meaningfully recover eigenspaces only when the eigenvalues are known with exceptional accuracy. During the later phases of the project, we learned firsthand why there are so few publications presenting data from Hamiltonian learning: it is inherently challenging to apply this approach to practical data."

The preliminary tests conducted by the researchers indicate that their proposed techniques may be scalable and effectively applicable to large quantum processors. Their findings could pave the way for similar methods aimed at characterizing the Hamiltonian parameters of quantum processors.

In their upcoming studies, Eisert and his colleagues intend to apply their methods to interacting quantum systems. They are also exploring the use of similar concepts derived from tensor networks in quantum systems made up of cold atoms, a concept originally introduced by physicist immanuel Bloch.

Broader Impact on Quantum Mechanics

The Importance of Knowing the Hamiltonian

"In my view, this field will be increasingly important in the future," stated Eisert. "A long-standing yet often undervalued question pertains to the nature of a system's Hamiltonian. This question is introduced in introductory quantum mechanics courses. Although it describes the system, it is typically assumed to be known, an assumption that is often erroneous."

"In the final analysis, experiments produce only data, meaning that in quantum mechanics, predictive capability exists only when the Hamiltonian is accurately defined. This leads to the inquiry of how one can extract it from the data."

Potential for High-Precision Quantum Simulations

In addition to enriching the conceptual framework surrounding Hamiltonian operators, the researchers' forthcoming studies may guide the evolution of quantum technologies. By facilitating the characterization of analog quantum simulators, they could unlock new pathways for achieving high-precision quantum simulations.

Conclusion

Quantum Systems and Their Future

"Analog quantum simulation enables the investigation of intricate quantum systems and materials by replicating them under highly controlled laboratory conditions," explained Eisert.

"This idea is meaningful--and linked to precise predictions--only when the Hamiltonian that characterizes the system is accurately known."

Source

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