Most production portfolio optimization is not purely continuous. Real portfolios have cardinality limits, minimum trade sizes, round lots, restricted lists, account-level cleanup rules, and other discrete edges. These are the places where classical convex optimization needs a refinement layer.

PRISM-Q is Asymmetry Computing's bounded discrete refinement layer. It is not a replacement for the classical production core. It sits on top of it, cleaning up hard combinatorial edges after the continuous problem has produced a high-quality candidate.

Figure 1: Where quantum-ready refinement fits

Continuous coreSolve the main convex risk, cost, and exposure problem.
Discrete edgeIdentify cardinality, lot, and cleanup constraints.
PRISM-QRun bounded refinement where combinatorial search matters.
Verified outputReturn a production portfolio with audit diagnostics.

Quantum-ready is an architecture choice

A system is quantum-ready when hard subproblems are isolated cleanly enough that better refinement methods can be inserted without rebuilding the entire workflow. Today that can mean GPU-accelerated heuristics and bounded discrete search. Over time, it can include quantum-inspired or quantum-assisted methods where they prove useful.

The important engineering move is separation: keep the continuous optimizer production-grade, then apply refinement only where the discrete structure justifies it.

Figure 2: Portfolio features that create discrete pressure

CardinalityHow many positions can the account hold?
Minimum tradesWhich small trades should be rounded, removed, or combined?
House rulesWhich client or platform constraints create non-smooth choices?

PRISM-Q helps where continuous solves are not enough

Continuous optimization is the right engine for most of the portfolio. It handles risk, return, tracking error, cost, and exposure efficiently. But final portfolio construction often needs a cleanup pass that respects bounded discrete requirements. That is where PRISM-Q belongs.

For buyers, the practical question is simple: which parts of your current workflow are continuous, and which parts are hidden manual cleanup? PRISM-Q is aimed at making those cleanup steps explicit, measurable, and easier to automate.

Practical next step: identify your top three discrete constraints and test them as separate refinement cases after a continuous PRISM solve.