The Daily Qubit

🖳 Move Over Silicon Valley, Quantum Valley Has Arrived

Author

Cierra Choucair
April 08, 2024 • Estimated Reading Time: 9 minutes

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Enjoy today’s breakdown of news, research, events & jobs within quantum.

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IN TODAY’S ISSUE:

  • Why New York is posed to be the next “Quantum Valley”

  • Quantum Monte Carlo simulations to enhance efficiency for both market and credit risk evaluations

  • Architecture that uses spin-photon entanglement to improve photon loss tolerance

  • Plus, an efficient and comprehensive breakdown on QNN’s from the community

TOP NEWS & RESEARCH

NEWS

WORLD’S FIRST UNIVERSITY QUANTUM COMPUTING SYSTEM

The IBM Quantum System One in RPI's Voorhees Computing Center, which was a cathedral in a former life. (Credit: IBM)

The Brief Byte: The first installation of a quantum computer within a university campus is bestowed upon the Rensselaer Polytechnic Institute, rather fittingly amidst celebrations of their bicentennial. The IBM Quantum System One will not only provide researchers and students with access to advanced computing capabilities, but also foster the development of a skilled workforce.

Highlights:

  1. The IBM Quantum System One has been unveiled in RPI’s Voorhees Computing Center. The system is powered by IBM Quantum Eagle processor which features 127 qubits in a heavy-hexagonal qubit layout, readout multiplexing, and scalable access wiring.

  2. RPI’s president hopes to establish the Capital Region as the “Quantum Valley” with the addition of the computer which will allow students the possibility of exploring new applications of quantum technology, discover new algorithms capable of quantum advantage, and leverage educational support from IBM to develop the next technology workforce.

RESEARCH

OVERVIEW OF “QUANTUM MONTE CARLO SIMULATIONS FOR FINANCIAL RISK ANALYTICS: SCENARIO GENERATION FOR EQUITY, RATE, AND CREDIT RISK FACTORS.”

The Brief Byte: Assuming fault-tolerant devices, this paper introduces Quantum Monte Carlo simulations that integrate risk factor evolution directly into quantum computations to generate probability distributions, offering a solution to overcome the high computational costs of traditional Monte Carlo simulations in financial risk management. By designing quantum circuits for various risk types, it demonstrates how quantum algorithms can enhance efficiency for both market and credit risk evaluations without needing pre-computed distributions.

Highlights:

  1. While many studies have focused on quantum circuits that begin with pre-computed probability distributions for financial risk analysis, directly encoding these distributions into a quantum state can be inefficient. Instead, this challenge is addressed by generating probability distributions directly within the quantum computation. This approach involves constructing quantum circuits that model the evolution of risk factors for equity, interest rates, and credit risk, thus enabling the direct generation of probability distributions and bypassing the need for pre-computed distributions, which would in turn reduce the reliance on classical computing and preserve the quantum advantage.

  2. The authors of the study focus on stochastic models for equity, interest rate, and credit risk factors to generate risk factor scenarios. For equities, they utilize geometric Brownian motion discretized with binomial trees; for interest rates, mean-reversion models discretized with bounded trinomial trees; and for credit risk, they calculate default probability using structural models based on binomial trees and survival probability from reduced-form models using a Poisson process. They demonstrate the convergence of measured values to expected values across these risk classes, validating the quantum computation's effectiveness in simulating financial risk scenarios.

  3. The study estimates that modeling financial risk accurately requires around 1200 qubits and a circuit depth of less than 108, achievable with future quantum computers, but challenging until fault-tolerant technology is available.

RESEARCH

OVERVIEW OF “LOSS-TOLERANT ARCHITECTURE FOR QUANTUM COMPUTING WITH QUANTUM EMITTERS”

The Brief Byte: Designed for emitters with limited memory, this research introduces a photonic quantum computing architecture that uses spin-photon entanglement and Bell measurements to build a large spin-qubit cluster state, enhancing photon loss tolerance and outperforming similar photonic approaches in efficiency.

Highlights:

  1. Leveraging spin-photon entanglement and large spin cluster states via Bell measurements on the photons, a measurement-based quantum computing architecture using photonic quantum emitters is introduced.

  2. This quantum computing architecture uses ballistic fusion processes and deterministic emitters with optimized lattices, improving photon loss tolerance and reducing dependence on probabilistic outcomes and classical feedback.

  3. The research conclusion suggests exploring broader lattice varieties, employing intricate resource states to enhance photon loss tolerance, quantifying and potentially minimizing the quantum emitters, and incorporating error correction methods to achieve a fault-tolerant architecture.

MORE BRIEF BYTES

ENTANGLED INSIGHTS

COMMUNITY RESOURCE

If you have five minutes, check out this quick, but beautifully well-done explanation by QML Engineer Jay Shah on quantum neural networks. Jay reviews the overall architecture and goes into detail on the flow within a QNN including both the hidden layers and output layers.

EVENTS

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