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Introducing Singularity by Multiverse Computing with Anzaetek

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Optimization – QiML – XVA, etc.

An approach driven by collaboration with customers is fueling the development of Singularity, a purpose-built software platform that
seamlessly integrates into the industrial value chain and effectively addresses production challenges.
Singularity is characterized by a range of high-level and low-level APIs designed to deliver a flexible and user-friendly experience.
At its core, it leverages sophisticated proprietary algorithms to combine quantum and quantum-inspired computing
to precisely and effectively solve complex AI and optimization challenges.

Unleashing the Potential of Singularity

  • Powerful:
    Production-Ready

    From on-premises to customer VPC integration,
    enjoy user-friendly, custom interfaces and
    flexible deployment options.

  • Deployable:
    Powerful Core Technology

    Leverage specialized algorithms for interpretable machine learning, deep learning, and optimization.
    This includes proprietary quantum-based routines that outperform commercial models.

  • Intuitive:
    Customer-Centric

    Experience proven results from a variety of real-world use cases, and gain further insights by exploring sections such as
    "User", "AI", and "Green Impact" in depth.

Solving Critical Challenges with Singularity

  • Faster, Cheaper, and More Efficient AI

    Innovating Deep Learning Costs with Tensor Networks
    The deep learning landscape is undergoing significant changes due to a sharp rise in computational costs. The recent training cost for large language models (LLM) has reached $100 million, and this cost doubles every 10 months. It is anticipated that a technological paradigm shift is essential to effectively train AI models, provided sufficient training data is secured.
    Accelerated Learning with Tensorized AI
    Singularity leverages tensor networks to revolutionize this scenario. Our tensorized models demonstrate accelerated learning, reduced data requirements, and lower power consumption. Achieving over 1000x acceleration, they revolutionize high-power applications such as machine vision and large language models.
    This makes tensorized AI indispensable in edge computing and plays a crucial role in autonomous vehicles. In particular, Multiverse Computing was recognized as one of the 100 most promising AI companies in the world for 2023 by CB Insights.

  • Explainable
    Machine Learning

    Unveiling Clarity: Addressing the Opacity of AI
    A pervasive issue in the AI domain is the lack of transparency in many machine learning (ML) models. Often, these models operate as enigmatic black boxes with little relevance to industrial applications.
    Singularity provides clarity by answering key questions:
    - To what extent can we trust our predictions?
    - What is the rationale behind specific decisions?
    - How do changes in circumstances affect the predictions?
    Enhancing Transparency in Mission-Critical Applications
    The importance of these answers cannot be overstated, especially in mission-critical applications where transparency is essential.
    By ensuring explainable machine learning, Singularity enables industries to trust and effectively leverage AI.

  • Faster, More Accurate
    Optimization

    Exploring Complex Optimization Challenges
    Optimization problems are at the core of various sectors, yet solving them is often challenging. These challenges can lead to inefficiencies across industrial and financial operations. In the green energy sector in particular, more precise and rapid optimization solutions for power distribution are key to driving change.
    Innovating Optimization with Hidden Data Structures
    Singularity adopts an innovative approach that leverages hidden data structures to converge faster than traditional algorithms.
    This disruptive potential has far-reaching implications, especially in time-sensitive optimization problems involving numerous variables.

Life Sciences

Molecular Simulation
Using tensor network algorithms, we determine the ground state energy of a given molecule or find the quantum Hamiltonian for various physical systems such as spin, boson, and fermion systems.
Athlete Injury Prediction
Coming soon! Utilizing quantum machine learning, we determine the short-term injury probability of athletes based on factors such as training load, game time, and rest.
Protein Design
Obtain the 3D protein structure for a given amino acid sequence.

Finance

Deep Pricing
Leverage deep learning models trained with quantum-inspired and other state-of-the-art methods to provide a parametric tool for obtaining the fair value, sensitivities, and other metrics of exotic derivative contracts.
Fair Pricing
Utilize ultra-fast quantum-inspired methods to evaluate fair price estimates for asset portfolios based on given market information and trends.
Index Tracking
Determine the optimal portfolio allocation to track the performance of financial indices while limiting the number of assets under management.
Portfolio Optimization
Use this Python and Excel package to obtain the optimal portfolio allocation that maximizes returns and minimizes risk while adhering to the investor's customized preferences and user-defined constraints.
Trading
Train a quantum machine learning model on historical FX trends, then apply the model in real time to obtain intraday trading signals.