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Functional Gradient-Based Geometric Curve Synthesis for Dynamic Quantum Error Suppression

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0022389
Agency Tracking Number: 0000271155
Amount: $1,645,510.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: C53-06a
Solicitation Number: N/A
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-04-03
Award End Date (Contract End Date): 2025-04-02
Small Business Information
4405 East West Hwy 410
Bethesda, MD 20814
United States
DUNS: 118063727
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Dennis Lucarelli
 (240) 988-9566
Business Contact
 Dennis Lucarelli
Phone: (240) 988-9566
Research Institution

C53-06a-271155Harnessing the quantum state of nature holds tremendous potential for creating new quantum technologies that surpass the capabilities of current systems. To create these new technologies, precise control over the quantum state must be maintained while simultaneously suppressing noise processes that would otherwise lead to decoherence and inaccurate results. This project will address the need for efficient algorithms and software tools that produce quantum control waveforms that suppress decoherence and control errors while implementing a universal set of quantum gates for near-term quantum computing. This research and development will enable commercialization of a recently discovered geometric framework for constructing noise-robust quantum control pulses that offers several advantages over current approaches. To facilitate customization and adoption, our optimal synthesis algorithms are implemented in open-source, machine learning software framework. In Phase I we developed proof-of-concept software for constructing error suppressing quantum gates. Theoretical advancements and extensions of the method to noise sources affecting specific quantum devices were derived. Open-source, interactive tutorials demonstrating feasibility and error-suppression performance of our algorithms were developed. Phase II will broaden the effort to include experimental partners to validate our R&D efforts and to develop industrial-grade, validated software tools required for creating noise-robust quantum algorithms. There is a nascent market for quantum control, calibration, and error suppression software tools for use by commercial customers seeking to improve the performance of their quantum algorithms and create value from near-term quantum computers. By partnering with leading quantum platform providers, the noise-suppressing quantum controls developed in this project will be available to commercial users and researchers increasing quantum algorithm run-times, decreasing cost, and providing more accurate results from near-term quantum computers.

* Information listed above is at the time of submission. *

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